The success of deep learning and machine learning (ML) in general in recent years is attributed to the availability of large datasets, more efficient algorithms, and specialized hardware [1]}. Based on the current hype, more and more companies try to integrate ML into their products, leading to an ever increasing demand of data scientists. Software developers who already know the targeted application domain could fill this gap but typically lack a scientific ML background. Therefore, they need guidance that helps them correctly use ML.
